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SceVar (Scenario Variations) database: Real World Statistics driven Scenario Variations for AV Testing in Simulation Abstraction of static and dynamic entities from road network-traffic ecosystem and their interactions or relationships in semantic data models for realistic simulation-based testing of AVs 21

SceVar (Scenario Variations) Database: Real World Statistics...

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13th ACM Web Science Conference (WebSci)

作者： Pathrudkar, Sagar Mukherjee, Saikat Indla, Vijaya Sarathi Chowdhury, Manish Siemens Technol India Bangalore Karnataka India

ISBN: (纸本)9781450385251

Autonomous vehicles (AV) are on a journey from incubation to a widespread usage. A major challenge for self-driving cars to reach widespread usage are guarantees about their safety and reliability. One approach to increasing safety and reliability is testing in simulation. However, simulation-based testing can be beneficial only if the simulations mimic the real-world phenomena. Additionally, AV environments are characterized by high-dimensionality, non-linearity, stochasticity, and non-stationarity, hence it is difficult in practice to exhaustively list and test all possible scenario variations. While this is a typical state space explosion problem, as usual it is essential to remove un-realistic scenarios from the test space, to reach a more realistic and plausible list of scenario variations. In this work, we present SceVar - a scenario variations database which analyzes real world driving data to extract realistic traffic patterns and driving behaviors which is then used to create massive number of scenario variations for (regression) testing in simulation. In addition, we also envisage that such statistical data can also be used by AV regulatory and testing agencies to certify vehicles for usage in specific operational design domains (ODDs).

关键词： Traffic Behavior Modelling semantic data models Statistical Analysis Simulation-based Testing of Autonomous Vehicles

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Framework and Algorithms for data Analytics, semantic Querying and Realistic Modelling of Traffic 9

Framework and Algorithms for Data Analytics, Semantic Queryi...

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9th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS)

作者： Pathrudkar, Sagar Schroeer, Guido Indla, Vijaya Sarathi Mukherjee, Saikat Siemens Technol Bangalore Karnataka India Siemens Mobil Munich Germany

ISBN: (纸本)9789897586521

Infrastructure elements would be crucial in enabling autonomous mobility at scale to provide centrally shared insights and possibly planning and control. Infrastructure mounted multi-sensor perception systems observe traffic and generate data in object list format which typically consists of timestamped vehicle trajectories and metadata about the vehicles, ie, their type, dimensions, etc. Such data is huge in volume and its analysis is difficult due to the spatiotemporal sequential nature of the data. In this work, we present framework and algorithms to semantically model and analyze this data in the context of map geometry to gain statistics and insights at an actionable level of abstraction. We start with algorithms to process common 2D-HDmap formats to extract map features - roads, lanes, junctions, etc. We then present meaningful traffic KPIs and statistics that describe traffic patterns. We finally describe methods to abstract the traffic patterns and driving behaviors into parametrized functions for various applications.

关键词： Traffic Analytics semantic data models Driving Behavior Real2Sim Simulation-Based Testing State Space Explosion

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Causal Relationship over Knowledge Graphs 22

Causal Relationship over Knowledge Graphs

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31st ACM International Conference on Information and Knowledge Management (CIKM)

作者： Huang, Hao Vidal, Maria-Esther Leibniz Univ Hannover TIB Leibniz Informat Ctr Sci & Technol Hannover Germany

ISBN: (纸本)9781450392365

Causality has been discussed for centuries, and the theory of causal inference over tabular data has been broadly studied and utilized in multiple disciplines. However, only a few works attempt to infer the causality while exploiting the meaning of the data represented in a data structure like knowledge graph. These works offer a glance at the possibilities of causal inference over knowledge graphs, but do not yet consider the metadata, e.g., cardinalities, class subsumption and overlap, and integrity constraints. We propose CareKG, a new formalism to express causal relationships among concepts, i.e., classes and relations, and enable causal queries over knowledge graphs using semantics of metadata. We empirically evaluate the expressiveness of CareKG in a synthetic knowledge graph concerning cardinalities, class subsumption and overlap, integrity constraints. Our initial results indicate that CareKG can represent and measure causal relations with some semantics which are uncovered by state-of-the-art approaches.

关键词： Causal inference Knowledge graphs semantic data models

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A Cloud-IoT Platform for Passive Radio Sensing: Challenges and Application Case Studies

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IEEE INTERNET OF THINGS JOURNAL 2018年第5期5卷 3624-3636页

作者： Kianoush, Sanaz Raja, Muneeba Savazzi, Stefano Sigg, Stephan Natl Res Council Italy Inst Elect Comp & Telecommun Engn I-20133 Milan Italy Aalto Univ Dept Commun & Networking Espoo 00076 Finland

We propose a platform for the integration of passive radio sensing and vision technologies into a cloud-IoT framework that performs real-time channel quality information (CQI) time series processing and analytics. Radio sensing and vision technologies allow to passively detect and track objects or persons by using radio waves as probe signals that encode a 2-D/3-D view of the environment they propagate through. View reconstruction from the received radio signals, or CQI, is based on real-time data processing tools, that combine multiple radio measurements from possibly heterogeneous IoT networks. The proposed platform is designed to efficiently store and analyze CQI time series of different types and provides formal semantics for CQI data manipulation-ontology models (OMs). Post-processed data can be then accessible to third parties via JSON-REST calls. Finally, the proposed system supports the reconfiguration of CQI data collection based on the respective application. The performance of the proposed tools are evaluated through two experimental case studies that focus on assisted living applications in a smart-space environment and on driver behavior recognition for in-car control services. Both studies adopt and compare different CQI manipulation models and radio devices as supported by current and future (5G) standards.

关键词： Cloud-assisted Internet of Things (IoT) passive radio sensing real-time data analysis semantic data models

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SMORE: Towards a semantic modeling for knowledge representation on social media

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SCIENCE OF COMPUTER PROGRAMMING 2016年 121卷 16-33页

作者： Villanueva, Daniel Gonzalez-Carrasco, Israel Lopez-Cuadrado, J. L. Lado, Nora Univ Carlos III Madrid Dept Comp Sci E-28903 Getafe Spain

This research presents SMORE, a semantic model for knowledge representation on social media. In order to provide recommendations, the model provides the elements for representing the content through the use of an ontological model and semantic techniques for the characterization and relationships between user profiles, products and social networks. In fact, with this model could be the basis of recommendation system based on social media data, and it could be exploited use by the recommendations on different products, which are stored in the Web and with similar characteristics between them. Moreover, SMORE represent the information related to user of the social networks in order to have a user characterization to be used for future recommendations in several domains. The semantic model has been evaluated with semantic data extracted from a trusted social network as, Twitter, obtaining the information specified by an expert in the field of marketing for recommendations in the automotive industry. (C) 2015 Elsevier B.V. All rights reserved.

关键词： semantic data models Social media semantic representation

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semantic Model of Possessive Pronouns Machine Translation for English to Bulgarian Language

Semantic Model of Possessive Pronouns Machine Translation fo...

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OnTheMove (OTM) Federated Conference

作者： Stoykova, Velislava Bulgarian Acad Sci Inst Bulgarian Language 52Shipchensky Proh StrBl 17 BU-1113 Sofia Bulgaria

ISBN: (纸本)9783319261386;9783319261379

The paper presents technique to interpret possessive pronouns for English to Bulgarian language syntax-based machine translation. It uses Universal Networking Language (UNL) as a formal framework to present possessive pronouns grammar features by employing semantic networks for both English and Bulgarian language. The technique includes also use of statistically-based estimation of accuracy of translation by measuring precision and recall of both training and controlled electronic text corpora and by improving related grammar rules.

关键词： Knowledge representation and reasoning semantic data models Knowledge-based natural language processing

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semantic-Guided Feature Selection for Industrial Automation Systems 14th

Semantic-Guided Feature Selection for Industrial Automation ...

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14th International semantic Web Conference (ISWC)

作者： Ringsquandl, Martin Lamparter, Steffen Brandt, Sebastian Hubauer, Thomas Lepratti, Raffaello Univ Munich Munich Germany Siemens AG Corp Technol Munich Germany Siemens AG Digital Factory Nurnberg Germany

ISBN: (纸本)9783319250106;9783319250090

Modern industrial automation systems incorporate a variety of interconnected sensors and actuators that contribute to the generation of vast amounts of data. Although valuable insights for plant operators and engineers can be gained from such data sets, they often remain undiscovered due to the problem of applying machine learning algorithms in high-dimensional feature spaces. Feature selection is concerned with obtaining subsets of the original data, e. g. by eliminating highly correlated features, in order to speed up processing time and increase model performance with less inclination to overfitting. In terms of high-dimensional data produced by automation systems, lots of dependencies between sensor measurements are already known to domain experts. By providing access to semantic data models for industrial data acquisition systems, we enable the explicit incorporation of such domain knowledge. In contrast to conventional techniques, this semantic feature selection approach can be carried out without looking at the actual data and facilitates an intuitive understanding of the learned models. In this paper we introduce two semantic- guided feature selection approaches for different data scenarios in industrial automation systems. We evaluate both approaches in a manufacturing use case and show competitive or even superior performance compared to conventional techniques.

关键词： semantic data models Feature selection Automation systems Machine learning

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Enhanced lod concepts for virtual 3D city models 8

Enhanced lod concepts for virtual 3D city models

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ISPRS 8th 3D GeoInfo Conference and WG II/2 Workshop

作者： Benner, Joachim Geiger, Andreas Gröger, Gerhard Häfele, Karl-Heinz Löwner, Marc-Oliver Institute for Applied Computer Science Karlsruhe Institute of Technology Germany Institute for Geodesy und Geoinformation University Bonn Germany Institute for Geodesy and Photogrammetry Technische Universität Braunschweig Germany

Virtual 3D city models contain digital three dimensional representations of city objects like buildings, streets or technical infrastructure. Because size and complexity of these models continuously grow, a Level of Detail (LoD) concept effectively supporting the partitioning of a complete model into alternative models of different complexity and providing metadata, addressing informational content, complexity and quality of each alternative model is indispensable. After a short overview on various LoD concepts, this paper discusses the existing LoD concept of the CityGML standard for 3D city models and identifies a number of deficits. Based on this analysis, an alternative concept is developed and illustrated with several examples. It differentiates between first, a Geometric Level of Detail (GLoD) and a semantic Level of Detail (SLoD), and second between the interior building and its exterior shell. Finally, a possible implementation of the new concept is demonstrated by means of an UML model. © 2013 Copernicus. All Rights Reserved.

关键词： CityGML Level of Detail semantic data models Virtual 3D city models

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A Meta-Analysis Comparing Relational and semantic models

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JOURNAL OF dataBASE MANAGEMENT 2011年第4期22卷 57-72页

作者： Siau, Keng Nah, Fiona Fui-Hoon Cao, Qing Univ Nebraska Lincoln NE 68583 USA Texas Tech Univ Rawls Coll Business Lubbock TX 79409 USA

data modeling is the sine quo non of systems development and one of the most widely researched topics in the database literature. In the past three decades, semantic data modeling has emerged as an alternative to traditional relational modeling. The majority of the research in data modeling suggests that the use of semantic data models leads to better performance;however, the findings are not conclusive and are sometimes inconsistent. The discrepancies that exist in the data modeling literature and the relatively low statistical power in the studies make meta-analysis a viable choice in analyzing and integrating the findings of these studies.

关键词： data Model Comprehension data Modeling Relational data Model semantic data models User Performance

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ENTITY-RELATIONSHIP MODELLING AND CARTOGRAPHIC TRANSCRIPTION

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Geodezija ir Kartografija 2003年第3期29卷 78-83页

作者： GiedrėBeconytė G.Beconytė

Suggestion of application of data models in thematic cartography, namely for cartographic transcription, was compilled by the stereotypes formed up in the past 10 years. People rarely expect carefully preserved semantics, esthetics or usability from “computer-made” maps. However, besides theoretical demonstrations there are formal methods that can help improve semantics, syntaxis and even esthetics of cartographic representation. The semantic model that has been constructed and used as intellectual aid in many spheres for quite a long time is presented here with its basic notions. The algorithm of cartographic transcription is presented, based on entity-relationship models of reality and corresponding graphic objects. Advantages of the method are summarised within the framework suggested by H. *** pastarąjį dešimtmetį susiformavę stereotipai ir tai, kad retai tikimasi didelio „kompiuterinių” kartografinių kūrinin semantinio tikslumo, estetinės kokybės ir patogumo naudoti, skatino bandymus teminėje kartografijoje taikyti duomenų modelius. Tačiau, be teorinio pagrindimo, yra ir formalių metodų, leidziančių labai pagerinti teminės kartografijos kūrinių semantiką sintaksę ir net estetiką. Straipsnyje aiškinamas vienas iš tokių metodų—semantinis duomenų modeliavimas. Pateikiamas šiuo metodu pagrįstos kartografinės transkripcijos algoritmas ir iliustruojama, kaip sudaromi realybės ir atitinkamn grafinių ženklų esybių-ryšių modellai bei kaip jie taikomi teminiam žemėlapiui sudaryti. Metodo privalumą akivaizdžiai rodo prof. H. Schlichtmann'o sudarytas kartografinės transkripcijos karkasas.

关键词： semantic data models entity-relationship modelling thematic cartography graphic variables semantiniai duomenų modeliai esybių-ryšių modeliavimas teminė kartografija grafiniai kintamieji

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